Relevant bibliographies by topics / Instantaneous frequency estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Instantaneous frequency estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Instantaneous frequency estimation"

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Katkovnik, V. "Nonparametric estimation of instantaneous frequency." IEEE Transactions on Information Theory 43, no. 1 (1997): 183–89. http://dx.doi.org/10.1109/18.567676.

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Lam, Daniel, Brandon Buckley, Cejo Lonappan, Asad Madni, and Bahram Jalali. "Ultra-wideband instantaneous frequency estimation." IEEE Instrumentation & Measurement Magazine 18, no. 2 (April 2015): 26–30. http://dx.doi.org/10.1109/mim.2015.7066680.

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Zhao, Dezun, Len Gelman, Fulei Chu, and Andrew Ball. "Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions." Sensors 20, no. 18 (September 11, 2020): 5201. http://dx.doi.org/10.3390/s20185201.

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It is proposed a novel instantaneous frequency estimation technology, multi-generalized demodulation transform, for non-stationary signals, whose true time variations of instantaneous frequencies are unknown and difficult to extract from the time-frequency representation due to essentially noisy environment. Theoretical bases of the novel instantaneous frequency estimation technology are created. The main innovations are summarized as: (a) novel instantaneous frequency estimation technology, multi-generalized demodulation transform, is proposed, (b) novel instantaneous frequency estimation results, obtained by simulation, for four types of amplitude and frequency modulated non-stationary single and multicomponent signals under strong background noise (signal to noise ratio is −5 dB), and (c) novel experimental instantaneous frequency estimation results for defect frequency of rolling bearings for multiple defect frequency harmonics, using the proposed technology in non-stationary conditions and in conditions of different levels of noise interference, including a strong noise interference. Quantitative instantaneous frequency estimation errors are employed to evaluate performance of the proposed IF estimation technology. Simulation and experimental estimation results show high effectiveness of the proposed estimation technology.
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Riedel, K. S. "Kernel estimation of the instantaneous frequency." IEEE Transactions on Signal Processing 42, no. 10 (1994): 2644–49. http://dx.doi.org/10.1109/78.324730.

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Yang, Senlin, and Jinghuai Gao. "Seismic Attenuation Estimation From Instantaneous Frequency." IEEE Geoscience and Remote Sensing Letters 7, no. 1 (January 2010): 113–17. http://dx.doi.org/10.1109/lgrs.2009.2028302.

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Qiu, Lunji, Haiyun Yang, and Soo-Ngee Koh. "Fundamental frequency determination based on instantaneous frequency estimation." Signal Processing 44, no. 2 (June 1995): 233–41. http://dx.doi.org/10.1016/0165-1684(95)00027-b.

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Lu, Wen-kai, and Chang-Kai Zhang. "Robust estimation of instantaneous phase using a time-frequency adaptive filter." GEOPHYSICS 78, no. 1 (January 1, 2013): O1—O7. http://dx.doi.org/10.1190/geo2011-0435.1.

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The instantaneous phase estimated by the Hilbert transform (HT) is susceptible to noise; we propose a robust approach for the estimation of instantaneous phase in noisy situations. The main procedure of the proposed method is applying an adaptive filter in time-frequency domain and calculating the analytic signal. By supposing that one frequency component with higher amplitude has higher signal-to-noise ratio, a zero-phase adaptive filter, which is constructed by using the time-frequency amplitude spectrum, enhances the frequency components with higher amplitudes and suppresses those with lower amplitudes. The estimation of instantaneous frequency, which is defined as the derivative of instantaneous phase, is also improved by the proposed robust instantaneous phase estimation method. Synthetic and field data sets are used to demonstrate the performance of the proposed method for the estimation of instantaneous phase and frequency, compared by the HT and short-time-Fourier-transform methods.
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BO, Lin. "Improved method for Hilbert instantaneous frequency estimation." Chinese Journal of Mechanical Engineering (English Edition) 20, no. 06 (2007): 94. http://dx.doi.org/10.3901/cjme.2007.06.094.

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Mejia-Barron, Arturo, David Granados-Lieberman, Jose Razo-Hernandez, Juan Amezquita-Sanchez, and Martin Valtierra-Rodriguez. "Harmonic PMU Algorithm Based on Complex Filters and Instantaneous Single-Sideband Modulation." Electronics 8, no. 2 (January 29, 2019): 135. http://dx.doi.org/10.3390/electronics8020135.

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Phasor measurement units (PMUs) have become powerful monitoring tools for many applications in smart grids. In order to address the different issues related to harmonics in power systems, the fundamental phasor estimator in a PMU has been extended to the harmonic phasor estimator by several researchers around the world. Yet, the development of harmonic phasor estimators is a challenge because they have to consider time-varying frequencies since the frequency deviation in the harmonic components is proportional to the harmonic order in a dynamic way. In this work, a new algorithm for harmonic phasor estimation using an instantaneous single-sideband (SSB) modulation is presented. Unlike other SSB-based approaches, its implementation in this work is based on concepts of instantaneous phase and instantaneous frequency. In general, the proposed algorithm is divided into two stages. Firstly, the estimation of the fundamental phasor is carried out by means of a complex finite impulse response (FIR) filter which provides the analytic signal used to compute the instantaneous magnitude, phase, and frequency. Secondly, a complex FIR filter bank is proposed for the estimation of the harmonic components, where the instantaneous SSB modulation technique is applied in order to center the harmonic components into specific narrow bands for each complex filter when an off-nominal frequency occurs. The validation of the proposed algorithm is carried out by means of the current standards of phasor measurement units, i.e., Std. C37.118.1-2011 and C37.118.1a-2014, which involve steady-state, dynamic, and time performance tests.
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Wang, Xiao-Feng, Da-Wei Li, Hui-Xu Dong, and Run-Lan Tian. "Instantaneous Frequency Estimation of Nonlinear FM Radar Signal Based on Multi-Scale Chirplet Path." Journal of Nanoelectronics and Optoelectronics 17, no. 2 (February 1, 2022): 285–97. http://dx.doi.org/10.1166/jno.2022.3196.

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Instantaneous frequency is an important parameter to non-linear frequency modulated (NLFM) signal in low probability intercept (LPI) radar. For electronic intelligence, it is very important to accurately estimate instantaneous frequency of NLFM signal. A multi-scale chirplet path pursuit (MCPP) method and its improved method are proposed for electronic intelligence systems to estimate instantaneous frequency of NLFM radar signal in this paper. Firstly, signal duration is divided into a set of dynamic time interval, multi-scale chirplet basis function is established on each time interval simultaneously. And then, projection coefficient in each dynamic interval is calculated basing on chirplet basis functions. And then, chirplet basis functions which have the largest projection coefficient with the analysis signal in each time interval are connected by path pursuit algorithm. Rough estimation of instantaneous frequency will be achieved by connecting the linear frequency of those chirplet basis functions. At last, to solve the problem that instantaneous frequency curve is not smooth for the impact of noise and chirplet errors, least square fitting method is used to further improve estimation accuracy. Experimental results show that, proposed improved MCPP algorithm is suitable for the instantaneous frequency of the NLFM radar signal at low SNR. Compared with time-frequency analysis method, it has higher estimation accuracy. Proposed method can also be applied to the instantaneous frequencies estimation of other NLFM signal without prior knowledge, such as seismic signals and fault diagnosis signals.
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Dissertations / Theses on the topic "Instantaneous frequency estimation"

1

Mai, Cuong. "Frequency Estimation Using Time-Frequency Based Methods." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/571.

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Any periodic signal can be decomposed into a sum of oscillating functions. Traditionally, cosine and sine segments have been used to represent a single period of the periodic signal (Fourier Series). In more general cases, each of these functions can be represented by a set of spectral parameters such as its amplitude, frequency, phase, and the variability of its instantaneous spectral components. The accuracy of these parameters depends on several processing variables such as resolution, noise level, and bias of the algorithm used. This thesis presents some background of existing frequency estimation techniques and proposes a new technique for estimating the instantaneous frequency of signals using short sinusoid-like basis functions. Furthermore, it also shows that the proposed algorithm can be implemented in a popular embedded DSPmicroprocessor for practical use. This algorithm can also be implemented using more complex features on more resourceful processing processors in order to improve estimation accuracy
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Abdoush, Yazan <1989&gt. "Time-Frequency Signal Analysis and Adaptive Instantaneous Frequency Estimation." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/9079/1/Thesis.pdf.

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Most of the human-made and physical signals have nonstationary spectra that evolve rapidly with time. To study and characterize such signals, the classic time-domain and frequency-domain representations are inadequate, since they do not provide joint time and frequency information; meaning that, they are signal representations in which the time and frequency variables are mutually exclusive. Time-frequency (TF) signal analysis (TFSA) concerns the processing of signals with time-varying spectral content. It allows for the construction of a signal representation in which the time and frequency variables are not averaged with respect to each other, but rather present together. This doctoral thesis has two main points of focus: TFSA based on a linear TF transform with progressive frequency-dependent resolution in the TF domain, known in the literature as the S-transform (ST), and designing adaptive methods for instantaneous frequency (IF) estimation, which is a fundamental concept in TFSA with numerous practical applications. The main original contributions are: 1- Modifications in the existing discrete definitions for implementing and inverting the ST to ensure exact invertibility and eliminate artifacts in the synthesized signal. 2- Derivation of an algorithm for least-squares signal synthesis form a modified discrete ST. 3- Formulation of a computationally efficient, fully discrete, and exactly invertible ST with a controllable TF sampling scheme, providing frequency resolution that can be varied and made as high as required. 4- Accuracy analysis of IF estimation based on a family of linear TF transforms that use Gaussian observation windows to localize the Fourier oscillatory kernel with arbitrarily defined standard deviations, and derivation of closed-form easily interpreted expressions for the bias and the variance of the estimation error. 5- Design of adaptive methods for IF estimation based on linear and quadratic TF representations.
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Hussain, Zahir M. "Adaptive instantaneous frequency estimation: Techniques and algorithms." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36137/7/36137_Digitised%20Thesis.pdf.

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This thesis deals with the problem of the instantaneous frequency (IF) estimation of sinusoidal signals. This topic plays significant role in signal processing and communications. Depending on the type of the signal, two major approaches are considered. For IF estimation of single-tone or digitally-modulated sinusoidal signals (like frequency shift keying signals) the approach of digital phase-locked loops (DPLLs) is considered, and this is Part-I of this thesis. For FM signals the approach of time-frequency analysis is considered, and this is Part-II of the thesis. In part-I we have utilized sinusoidal DPLLs with non-uniform sampling scheme as this type is widely used in communication systems. The digital tanlock loop (DTL) has introduced significant advantages over other existing DPLLs. In the last 10 years many efforts have been made to improve DTL performance. However, this loop and all of its modifications utilizes Hilbert transformer (HT) to produce a signal-independent 90-degree phase-shifted version of the input signal. Hilbert transformer can be realized approximately using a finite impulse response (FIR) digital filter. This realization introduces further complexity in the loop in addition to approximations and frequency limitations on the input signal. We have tried to avoid practical difficulties associated with the conventional tanlock scheme while keeping its advantages. A time-delay is utilized in the tanlock scheme of DTL to produce a signal-dependent phase shift. This gave rise to the time-delay digital tanlock loop (TDTL). Fixed point theorems are used to analyze the behavior of the new loop. As such TDTL combines the two major approaches in DPLLs: the non-linear approach of sinusoidal DPLL based on fixed point analysis, and the linear tanlock approach based on the arctan phase detection. TDTL preserves the main advantages of the DTL despite its reduced structure. An application of TDTL in FSK demodulation is also considered. This idea of replacing HT by a time-delay may be of interest in other signal processing systems. Hence we have analyzed and compared the behaviors of the HT and the time-delay in the presence of additive Gaussian noise. Based on the above analysis, the behavior of the first and second-order TDTLs has been analyzed in additive Gaussian noise. Since DPLLs need time for locking, they are normally not efficient in tracking the continuously changing frequencies of non-stationary signals, i.e. signals with time-varying spectra. Nonstationary signals are of importance in synthetic and real life applications. An example is the frequency-modulated (FM) signals widely used in communication systems. Part-II of this thesis is dedicated for the IF estimation of non-stationary signals. For such signals the classical spectral techniques break down, due to the time-varying nature of their spectra, and more advanced techniques should be utilized. For the purpose of instantaneous frequency estimation of non-stationary signals there are two major approaches: parametric and non-parametric. We chose the non-parametric approach which is based on time-frequency analysis. This approach is computationally less expensive and more effective in dealing with multicomponent signals, which are the main aim of this part of the thesis. A time-frequency distribution (TFD) of a signal is a two-dimensional transformation of the signal to the time-frequency domain. Multicomponent signals can be identified by multiple energy peaks in the time-frequency domain. Many real life and synthetic signals are of multicomponent nature and there is little in the literature concerning IF estimation of such signals. This is why we have concentrated on multicomponent signals in Part-H. An adaptive algorithm for IF estimation using the quadratic time-frequency distributions has been analyzed. A class of time-frequency distributions that are more suitable for this purpose has been proposed. The kernels of this class are time-only or one-dimensional, rather than the time-lag (two-dimensional) kernels. Hence this class has been named as the T -class. If the parameters of these TFDs are properly chosen, they are more efficient than the existing fixed-kernel TFDs in terms of resolution (energy concentration around the IF) and artifacts reduction. The T-distributions has been used in the IF adaptive algorithm and proved to be efficient in tracking rapidly changing frequencies. They also enables direct amplitude estimation for the components of a multicomponent
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El-Murr, George Mekhael. "Instantaneous Frequency Estimation Techniques in Sensorless Controlof AC Machines." Thesis, University of Newcastle Upon Tyne, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506554.

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Kadanna, Pally Roshin. "Implementation of Instantaneous Frequency Estimation based on Time-Varying AR Modeling." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/31978.

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Instantaneous Frequency (IF) estimation based on time-varying autoregressive (TVAR) modeling has been shown to perform well in practical scenarios when the IF variation is rapid and/or non-linear and only short data records are available for modeling. A challenging aspect of implementing IF estimation based on TVAR modeling is the efficient computation of the time-varying coefficients by solving a set of linear equations referred to as the generalized covariance equations. Conventional approaches such as Gaussian elimination or direct matrix inversion are computationally inefficient for solving such a system of equations especially when the covariance matrix has a high order. We implement two recursive algorithms for efficiently inverting the covariance matrix. First, we implement the Akaike algorithm which exploits the block-Toeplitz structure of the covariance matrix for its recursive inversion. In the second approach, we implement the Wax-Kailath algorithm that achieves a factor of 2 reduction over the Akaike algorithm in the number of recursions involved and the computational effort required to form the inverse matrix. Although a TVAR model works well for IF estimation of frequency modulated (FM) components in white noise, when the model is applied to a signal containing a finitely correlated signal in addition to the white noise, estimation performance degrades; especially when the correlated signal is not weak relative to the FM components. We propose a decorrelating TVAR (DTVAR) model based IF estimation and a DTVAR model based linear prediction error filter for FM interference rejection in a finitely correlated environment. Simulations show notable performance gains for a DTVAR model over the TVAR model for moderate to high SIRs.
Master of Science
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Caprio, James R., and Lennart Nystrom. "HIGH SPEED, WIDE BANDWIDTH SIGNAL DETECTION AND FREQUENCY ESTIMATION." International Foundation for Telemetering, 1986. http://hdl.handle.net/10150/615572.

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International Telemetering Conference Proceedings / October 13-16, 1986 / Riviera Hotel, Las Vegas, Nevada
A digital frequency discriminator (DFD) of the delay-correlator type is described. The device is shown to have an instantaneous frequency measurement capability on very short pulses. The theoretical performance of the DFD in a noisy background is derived and shown to compare favorably with measured results.
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Azemi, Ghasem. "Mobile velocity estimation using a time-frequency approach." Thesis, Queensland University of Technology, 2003. https://eprints.qut.edu.au/15807/1/Ghasem_Azemi_Thesis.pdf.

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This thesis deals with the problem of estimating the velocity of a mobile station (MS)in a mobile communication system using the instantaneous frequency (IF) of the received signal at the MS antenna. This estimate is essential for satisfactory handover performance, effective dynamic channel assignment, and optimisation of adaptive multiple access wireless receivers. Conventional methods for estimating the MS velocity are based either on the statistics of the envelope or quadrature components of the received signal. In chapter 4 of the thesis, we show that their performance deteriorates in the presence of shadowing. Other velocity estimators have also been proposed which require prior estimation of the channel or the average received power. These are generally difficult to obtain due to the non-stationary nature of the received signal. An appropriate window which depends on the unknown MS velocity must first be applied in order to accurately estimate the required quantities. Using the statistics of the IF of the received signal at the MS antenna given in chapter 3, new velocity estimators are proposed in chapter 4 of this thesis. The proposed estimators are based on the moments, zero-crossing rate, and covariance of the received IF. Since the IF of the received signal is not affected by any amplitude distortion, the proposed IF-based estimators are robust to shadowing and propagation path-loss. The estimators for the MS velocity in a macro- and micro-cellular system are presented separately. A macro-cell system can be considered as a special case of a micro-cell in which there is no line-of-sight component at the receiver antenna. It follows that those estimators which are derived for micro-cells can be used in a macro-cell as well. In chapter 4, we analyse the performance of the proposed velocity estimators in the presence of additive noise, non-isotropic scattering, and shadowing. We also prove analytically that the proposed velocity estimators outperform the existing methods in the presence of shadowing and additive noise. The proposed IF-based estimators need prior estimation of both the IF of the received signal and Ricean K-factor. The IF estimation in a typical wireless environment, can be considered as a special case of a general problem of IF estimation in the presence of multiplicative and additive noise. In chapter 5, we show that current time-frequency approaches to this problem which are based on the peak of a time-frequency distribution (TFD) of the signal, fail because of the special shape of the power spectral density of the multiplicative noise in a wireless environment. To overcome this drawback, the use of the first-order moment of a TFD is studied in chapter 5. Theoretical analysis and simulations show that the IF estimator based on the first-order moment of a TFD exhibits negligible bias when the signal-to-additive noise ratio is more than 10 dB. The Ricean K-factor is not only necessary for velocity estimation in micro-cells, but also is a measure of the severity of fading and a good indicator of the channel quality. Two new methods for estimating the Ricean K-factor based on the first two moments of the envelope of the received signal, are proposed in chapter 6. Performance analysis presented in chapter 6, prove that the proposed K estimators are robust to non-isotropic scattering. Theoretical analysis and simulations which are presented in chapters 4 and 7 of this thesis, prove that the proposed velocity and K estimators outperform existing estimators in the presence of shadowing and additive noise.
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Azemi, Ghasem. "Mobile Velocity Estimation Using a Time-Frequency Approach." Queensland University of Technology, 2003. http://eprints.qut.edu.au/15807/.

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This thesis deals with the problem of estimating the velocity of a mobile station (MS)in a mobile communication system using the instantaneous frequency (IF) of the received signal at the MS antenna. This estimate is essential for satisfactory handover performance, effective dynamic channel assignment, and optimisation of adaptive multiple access wireless receivers. Conventional methods for estimating the MS velocity are based either on the statistics of the envelope or quadrature components of the received signal. In chapter 4 of the thesis, we show that their performance deteriorates in the presence of shadowing. Other velocity estimators have also been proposed which require prior estimation of the channel or the average received power. These are generally difficult to obtain due to the non-stationary nature of the received signal. An appropriate window which depends on the unknown MS velocity must first be applied in order to accurately estimate the required quantities. Using the statistics of the IF of the received signal at the MS antenna given in chapter 3, new velocity estimators are proposed in chapter 4 of this thesis. The proposed estimators are based on the moments, zero-crossing rate, and covariance of the received IF. Since the IF of the received signal is not affected by any amplitude distortion, the proposed IF-based estimators are robust to shadowing and propagation path-loss. The estimators for the MS velocity in a macro- and micro-cellular system are presented separately. A macro-cell system can be considered as a special case of a micro-cell in which there is no line-of-sight component at the receiver antenna. It follows that those estimators which are derived for micro-cells can be used in a macro-cell as well. In chapter 4, we analyse the performance of the proposed velocity estimators in the presence of additive noise, non-isotropic scattering, and shadowing. We also prove analytically that the proposed velocity estimators outperform the existing methods in the presence of shadowing and additive noise. The proposed IF-based estimators need prior estimation of both the IF of the received signal and Ricean K-factor. The IF estimation in a typical wireless environment, can be considered as a special case of a general problem of IF estimation in the presence of multiplicative and additive noise. In chapter 5, we show that current time-frequency approaches to this problem which are based on the peak of a time-frequency distribution (TFD) of the signal, fail because of the special shape of the power spectral density of the multiplicative noise in a wireless environment. To overcome this drawback, the use of the first-order moment of a TFD is studied in chapter 5. Theoretical analysis and simulations show that the IF estimator based on the first-order moment of a TFD exhibits negligible bias when the signal-to-additive noise ratio is more than 10 dB. The Ricean K-factor is not only necessary for velocity estimation in micro-cells, but also is a measure of the severity of fading and a good indicator of the channel quality. Two new methods for estimating the Ricean K-factor based on the first two moments of the envelope of the received signal, are proposed in chapter 6. Performance analysis presented in chapter 6, prove that the proposed K estimators are robust to non-isotropic scattering. Theoretical analysis and simulations which are presented in chapters 4 and 7 of this thesis, prove that the proposed velocity and K estimators outperform existing estimators in the presence of shadowing and additive noise.
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Nguyen, Linh Trung. "Estimation and separation of linear frequency- modulated signals in wireless communications using time - frequency signal processing." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15984/.

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Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.
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Nguyen, Linh-Trung. "Estimation and separation of linear frequency- modulated signals in wireless communications using time - frequency signal processing." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/15984/1/Nguyen_Linh-Trung_Thesis.pdf.

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Signal processing has been playing a key role in providing solutions to key problems encountered in communications, in general, and in wireless communications, in particular. Time-Frequency Signal Processing (TFSP) provides eective tools for analyzing nonstationary signals where the frequency content of signals varies in time as well as for analyzing linear time-varying systems. This research aimed at exploiting the advantages of TFSP, in dealing with nonstationary signals, into the fundamental issues of signal processing, namely the signal estimation and signal separation. In particular, it has investigated the problems of (i) the Instantaneous Frequency (IF) estimation of Linear Frequency-Modulated (LFM) signals corrupted in complex-valued zero-mean Multiplicative Noise (MN), and (ii) the Underdetermined Blind Source Separation (UBSS) of LFM signals, while focusing onto the fast-growing area of Wireless Communications (WCom). A common problem in the issue of signal estimation is the estimation of the frequency of Frequency-Modulated signals which are seen in many engineering and real-life applications. Accurate frequency estimation leads to accurate recovery of the true information. In some applications, the random amplitude modulation shows up when the medium is dispersive and/or when the assumption of point target is not valid; the original signal is considered to be corrupted by an MN process thus seriously aecting the recovery of the information-bearing frequency. The IF estimation of nonstationary signals corrupted by complex-valued zero-mean MN was investigated in this research. We have proposed a Second-Order Statistics approach, rather than a Higher-Order Statistics approach, for IF estimation using Time-Frequency Distributions (TFDs). The main assumption was that the autocorrelation function of the MN is real-valued but not necessarily positive (i.e. the spectrum of the MN is symmetric but does not necessary has the highest peak at zero frequency). The estimation performance was analyzed in terms of bias and variance, and compared between four dierent TFDs: Wigner-Ville Distribution, Spectrogram, Choi-Williams Distribution and Modified B Distribution. To further improve the estimation, we proposed to use the Multiple Signal Classification algorithm and showed its better performance. It was shown that the Modified B Distribution performance was the best for Signal-to-Noise Ratio less than 10dB. In the issue of signal separation, a new research direction called Blind Source Separation (BSS) has emerged over the last decade. BSS is a fundamental technique in array signal processing aiming at recovering unobserved signals or sources from observed mixtures exploiting only the assumption of mutual independence between the signals. The term "blind" indicates that neither the structure of the mixtures nor the source signals are known to the receivers. Applications of BSS are seen in, for example, radar and sonar, communications, speech processing, biomedical signal processing. In the case of nonstationary signals, a TF structure forcing approach was introduced by Belouchrani and Amin by defining the Spatial Time- Frequency Distribution (STFD), which combines both TF diversity and spatial diversity. The benefit of STFD in an environment of nonstationary signals is the direct exploitation of the information brought by the nonstationarity of the signals. A drawback of most BSS algorithms is that they fail to separate sources in situations where there are more sources than sensors, referred to as UBSS. The UBSS of nonstationary signals was investigated in this research. We have presented a new approach for blind separation of nonstationary sources using their TFDs. The separation algorithm is based on a vector clustering procedure that estimates the source TFDs by grouping together the TF points corresponding to "closely spaced" spatial directions. Simulations illustrate the performances of the proposed method for the underdetermined blind separation of FM signals. The method developed in this research represents a new research direction for solving the UBSS problem. The successful results obtained in the research development of the above two problems has led to a conclusion that TFSP is useful for WCom. Future research directions were also proposed.
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Book chapters on the topic "Instantaneous frequency estimation"

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Hansen, M., K. Daniilidis, and G. Sommer. "Optimization of stereo disparity estimation using the instantaneous frequency." In Computer Analysis of Images and Patterns, 321–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63460-6_133.

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Malarvili, M. B., L. Rankine, M. Mesbah, P. B. Colditz, and B. Boashash. "Heart Rate Variability Characterization Using a Time-Frequency Based Instantaneous Frequency Estimation Technique." In 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 455–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-68017-8_115.

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Kuether, Robert J., and Matthew R. W. Brake. "Parameter Estimation via Instantaneous Frequency and Damping from Transient Ring-Down Data." In The Mechanics of Jointed Structures, 381–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56818-8_21.

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Roy, Sankar Kumar. "Improved Fourier Polynomial Based Phase Modeling for Estimating Instantaneous Frequency from a Noisy FM Signal." In Lecture Notes in Mechanical Engineering, 107–20. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6577-5_12.

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"Instantaneous Frequency Estimation and Localization." In Time Frequency Analysis, 421–64. Elsevier, 2003. http://dx.doi.org/10.1016/b978-008044335-5/50031-0.

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"Instantaneous Frequency Estimation and Localization." In Time-Frequency Signal Analysis and Processing, 575–635. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-398499-9.00010-8.

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Lazzarini, Victor. "Time-Frequency Processing." In Spectral Music Design, 156–203. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197524015.003.0006.

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The idea of dynamic spectral processing, introduced at the end of the previous chapter is fully developed here. The principle of sub-band analysis and synthesis is shown as the basis for a time-varying frequency-domain approach. The short-time Fourier transform (STFT) is introduced as a sequence of time-ordered DFT frames from which amplitude and phase data can be obtained. Different methods for instantaneous frequency estimation are discussed. A streaming system for dynamic spectral processing is introduced, and various modification techniques are explored. The latter part of the chapter presents the Hilbert transform as yet another streaming spectral processing application. The chapter concludes with further additions to the notions of spectrum developed earlier in the volume.
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Mocenni, Chiara, and Angelo Facchini. "Recurrence Indicators for the Estimation of Characteristic Size and Frequency of Spatial Patterns." In Complexity Science, Living Systems, and Reflexing Interfaces, 209–17. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2077-3.ch010.

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In this chapter, the authors propose a method for the estimation of the characteristic size and frequency of the typical structure in systems showing two dimensional spatial patterns. In particular, they use several indicators caught from the nonlinear framework for identifying the small and large scales of the systems. The indicators are applied to the images corresponding to the instantaneous realization of the system. The method assumes that it is possible to capture the main system’s properties from the distribution of the recurring patterns in the image and does not require the knowledge of the dynamical system generating the patterns neither the application of any image segmentation method.
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"Iterative estimator of instantaneous frequency of multiple Linear Frequency Modulation signals." In Control Engineering and Information Systems, 245–48. CRC Press, 2015. http://dx.doi.org/10.1201/b17732-48.

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Conference papers on the topic "Instantaneous frequency estimation"

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Aamir, Khalid, and Mohammad Maud. "Instantaneous Frequency Estimation from Signal Model." In 2006 International Conference on Emerging Technologies. IEEE, 2006. http://dx.doi.org/10.1109/icet.2006.335949.

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Vesnaver, A. L., and E. Poggiagliolmi. "Anelastic Absorption Estimation by Instantaneous Frequency." In 77th EAGE Conference and Exhibition 2015. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201413063.

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Lu, W., and C. K. Zhang. "A Robust Instantaneous Frequency Estimation Method." In 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011. Netherlands: EAGE Publications BV, 2011. http://dx.doi.org/10.3997/2214-4609.20149432.

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Tanaka, Kobayashi, Arifianto, and Masuko. "Fundamental frequency estimation based on instantaneous frequency amplitude spectrum." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005743.

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Tanaka, Tomohiro, Takao Kobayashi, Dhany Arifianto, and Takashi Masuko. "Fundamental frequency estimation based on instantaneous frequency amplitude spectrum." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743721.

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Hsu, Ming-Kuang, and J. C. Sheu. "Instantaneous frequency estimation using Osculating Circle Method." In 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6100679.

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Kaniewska, Magdalena. "Online pitch estimation using instantaneous complex frequency." In 2011 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2011. http://dx.doi.org/10.1109/ecctd.2011.6043369.

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Li, Ying, Antonia Papandreou-Suppappola, and Darryl Morrell. "Instantaneous Frequency Estimation Using Sequential Bayesian Techniques." In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. IEEE, 2006. http://dx.doi.org/10.1109/acssc.2006.354812.

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Li, Y., D. Simon, A. Papandreou-Suppappola, D. Morrell, and R. L. Murray. "Sequential MCMC Estimation of Nonlinear Instantaneous Frequency." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367052.

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Odegard, Jan E., Richard G. Baraniuk, and Kurt L. Oehler. "Instantaneous frequency estimation using the reassignment method." In SEG Technical Program Expanded Abstracts 1997. Society of Exploration Geophysicists, 1997. http://dx.doi.org/10.1190/1.1885824.

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